AB0188 A BIOINFORMATIC APPROACH TO IDENTIFYING SENSITIZING THERAPEUTICS FOR RHEUMATOID ARTHRITIS
Authors: J. Rajan, D. Gibson, S. D. Zhang, A. Bjourson
Journal: Annals of the Rheumatic Diseases (2023)

<h3>Background</h3> Rheumatoid Arthritis (RA) is a systemic and chronic autoimmune disorder that has significant impact on quality of life and work capacity for the patient. Treatment and management of RA is aimed at gaining control of inflammation and alleviating pain, however, achieving remission and low disease activity with minimal toxicity is frequently not possible with the current suite of drugs. Additionally with escalating de novo drug development costs, alternate bioinformatic approaches which scope the potential to repurpose already licenced compounds and their ability to work synergistically have become attractive. <h3>Objectives:</h3> 1)Develop, test and refine a bespoke connectivity mapping based bioinformatic pipeline, DrugExpress, with capability of mining treatment response gene signatures using public RNAseq and microarray datasets. 2)Verify treatment response signatures and prioritise novel drugs to generate a list of robust sensitising drugs. <h3>Methods</h3> Public RNAseq (GSE198529) and microarray datasets (GSE93777 and GSE24742) were mined based on the presence of DAS score, clinical features, sample size and technological platform. R/Bioconductor packages and Perseus software were used to pre-process and carry out differential expression (DE) analysis to obtain a list of differentially expressed genes (DEGs), based on the cut off criteria of padj &lt; 0.05 and a minimal 2-fold change of expression, and derive gene signatures characteristic of treatment response. DEGs from multiple datasets were combined to create a master gene list consisting of 21 DEGs. Gene Ontology (GO) enrichment analysis was performed on the genes in the master list to identify the top biological pathways. A list of inferred genes involved in each of these pathways was compiled to determine the overall direction of expression with response to treatment. DEGs in the master list were extracted and mapped to Affymetrix probeset IDs to create treatment response gene signatures to be entered into a sscMap search for candidate drugs. Connectivity mapping (CMap) analysis was used to establish networks between DEGs in the gene signature and FDA approved drugs. P-value and connection score of each reference drug in the CMap was obtained and used to determine statistical significance and perturbation stability. <h3>Results</h3> GO enrichment analysis performed on the master gene list identified the top 3 pathways: transport of connexons to the plasma membrane, oligomerization of connexins into connexons, and gap junction assembly. CMap analysis identified a total of 36 statistically significant compounds with a perturbation stability score of 1. Candidate compounds were selected based on whether they enhanced a theoretical response phenotype. The top 3 ranked compounds (by p value) which would induce theoretical reduction in disease activity were: ciclosporin (p-value = 0.0001), demecarium bromide (p-value = 0.0002) and 2-aminobenzenesulfonamide (p-value = 0.0002). <h3>Conclusion</h3> The analysis using the DrugExpress pipeline illustrates the process of treatment response DEG extraction from public expression datasets. These response signatures can be used for CMap analysis to predict new compounds for treatment refractive patients and can potentially simulate the effective changes observed in responders. Next step in the pipeline would be to implement <i>in silico</i> toxicity screening methods to identify any contraindications with existing treatments. <h3>References</h3> [1]Rosa-Gonçalves, D., Bernardes, M. and Costa, L. (2018) Quality of life and functional capacity in patients with rheumatoid arthritis - Cross-sectional study. Reumatologia Clinica, 14(6), 360-366. [2]Scott, I.C., Whittle, R., Bailey, J., Twohig, H., Hider, S.L., Mallen, C.D., Muller, S. and Jordan, K.P. (2022) Rheumatoid arthritis, psoriatic arthritis, and axial spondyloarthritis epidemiology in England from 2004 to 2020: An observational study using primary care electronic health record data. The Lancet Regional Health. Europe, 23, 100519. [3]Versus Arthritis. (2019) The State of Musculoskeletal Health 2019. <h3>Acknowledgements:</h3> NIL. <h3>Disclosure of Interests</h3> None Declared.